Method for identifying color in machine and computer vision applications

ABSTRACT

A method for identifying color in a target creates a ratio color space by determining the largest color component value of each pixel in an image and creating a ratio of all of the color component value with the largest component value for each pixel. The ratio for the color component of each pixel undergoes a threshold test to identify each color component as a rich shade or a fade shade. The ratio space color components are converted to a black and white image. Color information of adjacent pixels are clumped together to form blobs of the same color. The blobs are filtered by shape, color, location, or orientation and sorted to find targets that consist of a predefined pattern with the desired characteristics.

CROSS REFERENCE TO CO-PENDING APPLICATION

This application claims priority benefit to the filing date ofco-pending U.S. Provisional Patent Application Ser. No. 60/917,966,filed May 15, 2007.

BACKGROUND

The present invention relates to a method for identifying colors in animage. The image is in the field of view of a camera or cameras and thecamera is an interface to a computer for machine and computer visionapplications. The invention also relates to a triggering mechanism inthe field of view by identifying colors which allows the camera to beused as an interface with a computer for consumer applications.

Machine vision, commonly called automated inspection, has been used inmanufacturing processes to improve productivity and quality. On atypical production line, a sensor detects a part and signals a videocamera positioned above or to the side of the inspection point tocapture an image and send it to a machine vision processor. Using acombination of machine vision software and hardware, the vision systemanalyzes the image and provides mathematical answers about the part. Atraditional grayscale machine vision technology makes decisions based on0-256 shades of gray. A typical vision algorithm segments an image intopixels that fall within an intensity band bounded by a lower and anupper threshold from the irrelevant pixels that have intensities outsideof this intensity band. Alternatively they look at the rate of change ofthe image pixels. Once the relevant pixels have been identified,adjacent pixels are clumped together to form blobs or geometric edgesand these are then characterized by geometric characteristics such aslocation, size, shape, etc. Inspecting colored parts or objects withgrayscale machine vision system becomes usually unreliable in many casesand impossible in others. For this reason, use of a color machine visiontechnology is needed to inspect parts or objects in ways that could notbe done using traditional grayscale machine vision systems.

Thus far, color machine vision systems have been used for three primaryvision applications:

Color Matching—verifying that a certain part's or object's color matcheswhat the vision system is programmed to find

Color Sorting—sorting parts or objects based on color

Color Inspection—inspecting colored parts or objects for defects orimperfections that grayscale image processing tools can't detect.

Defined as the perceptual result of visible light reflected from anobject to our eyes, color represents an interpretive concept. Dependingon how light is reflected, we all see colors a bit differently. Humanvisual system uses color to draw conclusions about surfaces, boundariesand location of objects in a scene. It takes three things to see color:a light source, a sample or an object, and a detector such as an eye ora camera. Color derives from the spectrum of light (distribution oflight energy versus wavelength) interacting in the eye with the spectralsensitivities of light receptors. Typically, a wavelength spectrum from380 nm to 740 nm (roughly) of light is detectable by human eye. Thisrange is known as the visible light. The pure “spectral colors” from acontinuous spectrum can be divided into distinct colors: violet(˜380-440 nm), blue (˜440-485 nm), cyan (˜485-500 nm), green (˜500-565nm), yellow (˜565-590 nm), orange (˜590-625 nm), and red (˜625-740 nm).However, these ranges are not fixed, the division is a matter ofculture, taste, and language. For instance, Newton added a seventhcolor, indigo, as wavelengths of 420-440 nm between blue and violet, butmost people are not able to distinguish it. Of course, there are manycolor perceptions that by definition cannot be pure spectral colors.Some examples of non-spectral colors are the “achromatic colors” (black,gray, and white) and colors such as pink, tan, and magenta.

An additive color system involves light “emitted” from a source orilluminant of some sort such as TV or computer monitor. The additivereproduction process usually uses red, green, and blue which are the“primary colors” to produce the other colors. Combining one of theseprimary colors with another in equal amounts produces the “secondarycolors” cyan, magenta, and yellow. Combining all three primary lights(colors) in equal intensities produces white. Varying the luminosity ofeach light (color) eventually reveals the full gamut of those threelights (colors).

Results obtained when mixing additive colors are often counterintuitivefor people accustomed to the more everyday subtractive color system ofpigments, dyes, inks, and other substances which present color to theeye by “reflection” rather than emission. Anything that is not additivecolor is subtractive color.

Light arriving at an opaque surface is either “reflected”, “scattered”,or “absorbed” or some combination of these. Opaque objects that do notreflect specularly (that is, in a manner of a mirror) have their colordetermined by which wavelengths of light they scatter more and whichthey scatter less. The light that is not scattered is absorbed. Ifobjects scatter all wavelengths, they appear white. If they absorb allwavelengths, they appear black. Objects that transmit light are eithertranslucent (scattering the transmitted light) or transparent (notscattering the light).

The color of an object is a complex result of its surface properties,its transmission properties, and its emission properties, all of whichfactors contribute to the mix of wavelengths in the light leaving thesurface of an object. The perceived color is then further conditioned bythe nature of the ambient illumination, and by the color properties ofother objects nearby; and finally, by the permanent and transientcharacteristics of the perceiving eye and brain.

Light, no matter how complex its composition of wavelengths, is reducedto three color-components by the eye. For each location in the visualfield, the three types of color receptor cones in the retina yield threesignals based on the extent to which each is stimulated. These valuesare sometimes called “tristimulus values”.

To analyze and process images in color, machine vision systems typicallyuse data from color spaces such as RGB, HSI (or HSL), HSV (or HSB),CIELAB (or CIEXYZ) CMYK, etc. In the RGB color space, each color appearsin its primary spectral components of red, green, and blue. Whencombined with a three-dimensional coordinate system, the RGB color spacedefines quantitatively any color on the spectrum. RGB uses “additive”color mixing. X-axis specifies the amount of red color, Y-axis specifiesthe amount of green and the Z-axis specifies amount of blue. If RGBcolor model is implemented in 256 (0 to 255) discrete levels of eachcolor component (8 bits) then the color space defines a gamut of256×256×256 or about 16.7 million colors.

The HSI color space, also known as HSL is broken down into hue,saturation and intensity or lightness. Hue refers to pure color,saturation refers to the degree or color contrast, and intensity refersto color brightness.

HSV (hue, saturation, value), also known as SHB (hue, saturation,brightness), is quite similar to HSL “brightness” replacing “lightness”.Artists often use HSV color space because it is more natural to thinkabout a color in terms of hue and saturation.

CIE 1931 XYZ color space is the first attempt to produce a color spacebased on measurements of human color perception. It is the most completecolor space used conventionally to describe all the colors visible tohuman eye. It was developed by the “International Commission onIllumination” (CIE). CIE 1976 LAB is based directly on CIE 1931 XYZcolor space as an attempt to make the perceptibility of colordifferences linear. CIE is the most accurate color space but is toocomplex for everyday uses.

CMYK uses subtractive color mixing in used printing process. It ispossible to achieve a large range of colors seen by humans by combiningcyan, magenta, and yellow transparent dyes/inks on a white substrate.Often a fourth black is added to improve reproduction of some darkcolors. CMYK stores ink values for cyan, magenta, yellow, and black.There are many CMYK color spaces for different sets of inks, substrates,and press characteristics.

Although dozens of defined color spaces exist, color machine visionapplications primarily have used RGB and HSI or HSV color spaces.

Prior art systems use various techniques to measure and match colorssuch as discusses a color sorting method for wires by comparing theoutput signal of a camera to the intensity ratio of known colors until asubstantial match is found.

Another technique provides a color sorting system and method used forsorting fruits and vegetables. The sorting process is handled with alook up table. The pixel value of the input image is sent to the look uptable and the output from the look up table is either series of 0's(accept) or 1's (reject).

Another method for automatically and quantitatively measuring colordifference between a color distribution of an object and a referencecolor image uses “color distance” in a color system. A templaterepresenting the reference color image is stored in a memory of amachine vision system. The machine vision system generates a samplecolor image of the object and processes the template together with thesample color image to obtain a total color distance.

An apparatus is known for sorting fragments of titanium-based sponge onthe basis color by comparing the color values of the image to a set ofdata values stored in a look up table for rejection or acceptance ofeach fragment.

Another system and method for locating regions in a target image bymatching a template image with respect to color and pattern informationeither by using a hill-climbing technique or fuzzy logic.

A different system and method of perceptual color identification can beused for the identification and tracking of objects, for example, in asurveillance video system. The described method includes a multilevelanalysis for determining the perceptual color of an object based onobserved colors. This multilevel analysis can include a pixel level, aframe level, and/or a sequence level. The determination makes use ofcolor drift matrices and trained functions such as statisticalprobability functions. The color drift tables and function training arebased on training data generated by observing objects of knownperceptual color in a variety of circumstances.

It is clear from the prior art that traditional grayscale machine visionsystems are being used successfully in a wide variety of inspection andprocess control applications for the electronic, automotive, foodproducts, packaging, pharmaceutical, and recycling industries. However,the use of color machine vision systems in these industries has onlybeen applicable to well controlled immediate environments orsurroundings. It is also clear that prior art relied on matching colorto a reference color image or template. A color machine and computervision system that can make robust identification of color under varyinglighting and changing image shift, scale, and rotation conditions isdesirable. Machine vision systems use specialized and expensive hardwareand software and therefore their use has been limited to industrialapplications. With the advance of inexpensive color webcams, it is alsodesirable to find use for computer vision systems in cost sensitiveconsumer applications.

It would desirable to provide a method for identifying color in anordinary lighting environment, to thereby obviate the prior art use ofproviding powered light sources in the target or in an illuminatingsource with specific directional or color characteristics.

It would also be desirable to provide an improved method for effectivelyand accurately identifying color of a target image for machine andcomputer vision applications under varying lighting and imageconditions.

It would also be desirable to provide a method for triggeringinteraction or applications between a user and computer by identifyingcolored blobs or objects of a target image in the field of view of acamera or cameras.

It would be desirable to provide a method for tracking an object such asa pen in the field of view by identifying relative location of coloredsections of this object.

It would be desirable to provide a machine or computer vision systemthat can be used in consumer applications by identifying color.

SUMMARY

A method of identifying color including the steps of defining a ratiocolor space including determining the largest color component for eachpixel in an image, and dividing all of the color components values ofeach pixel by the largest color component value of each pixel.

The method further includes the steps of developing a filter for usewith a color space including creating a threshold black and white imagefor one color of interest in each pixel of an image setting thethreshold so that pale shades of the color of interest are separatedfrom rich shades of the color of interest and repeating the steps foreach color component in each pixel.

If any color component value for each pixel of the images is above orbelow the threshold, the method sets all three color components for eachpixel to black; and if all color components of each pixel of the imagematches the threshold, all three color components are set to white.

The method further includes the step of performing a threshold test onthe ratio of space values of each pixel.

The method further includes the step of solving a distance equation interms of ratio color components for each pixel of the target accordingto:Dist=POS(Sr*(r−Tr))+POS(Sg*(g−Tg))+POS(Sb*(b−Tb))

where Sr, Sg, and Sb are scale parameters for the primary colorcomponent values r, g and b, Tr, Tg and Tb are threshold values for eachof the color components r, g and b. and POS (q)=0 if q is less than orequal to 0, POS (q)=q.

In another aspect, a robust method using a computer to rapidly find thelocation of a colored target within an image includes one or more of thesteps of as a one time step prior to processing a set of images,defining a color ratio space and creating a corresponding look-up-tablefor each primary color and secondary color used in a target capturing animage and subtracting from each pixel in the image the bias of eachcamera color component apply the ratio space look-up-table to each pixelin the image for each primary and each secondary color used in thetarget clumping together the adjacent color information of adjacentpixels to form blobs of the same color filtering the blobs of the samecolor by at least one of shape, size, location and orientation sortingall of the filtered blobs to find targets that consist of a predefinedpattern formed of a set of different colored blobs with at least one ofa specific size and a specific shape at relative locations andorientations to each other tracking a plurality of targets to determineone of the absolute and the relative location and to determine if anychanges occurred in one of a sequence of images or compared to an idealimage and using the changes of interest introduced to the field of viewas a triggering mechanism to run macros or applications or to initiateinteraction between the user and the computer.

BRIEF DESCRIPTION OF THE DRAWING

The various features, advantages and other uses of the present inventionwill become more apparent by referring to the following detaileddescription and drawing in which:

FIG. 1 illustrates a computer vision system which performs coloridentification; and

FIG. 2 illustrates a computer vision system that performs colortracking.

DETAILED DESCRIPTION

FIG. 1 illustrates one aspect of a computer vision system 10 thatperforms color identification. The computer vision system 10 may includea computer system 11, a color camera 12, such as a webcam, and a fieldof view 13.

The computer system 11 may include one or more processors, a memorymedium, monitor, and input devices, such as a keyboard and mouse and anyother components necessary for a computer system. The computer system 11also includes one or more software programs operable to perform coloridentification function. The software programs may be stored in a memorymedium, such as a DRAM, SRAM, EDO RAM, etc., or a magnetic medium suchas a hard drive, DVD, CD, or floppy disk. The computer system 11 isbroadly defined to encompass any device, having a processor whichexecutes instructions from a memory medium, such as a personal computer,workstation, mainframe computer, network appliance, internet appliance,personal digital assistant (PDA), cell phone, ipod, etc.

The color camera 12 can be an inexpensive webcam. The color camera 12may comprise an image sensor such as a “Charged Coupled Device” (CCD) or“Complementary Metal Oxide Semiconductor” (CMOS). The color camera 12may be connected to the computer system 11 USB port either through awire or wirelessly. The color camera 12 may be attached to a flexiblestand or clipped on a monitor to point at a particular field of view 13.The output of the color camera 12 is usually the values in 256 discretelevels of each of three color-components, red, green and blue (R, G, B),for each pixel of a target image in the field of view 13. Thepixel-by-pixel color information of the target image is fed to thecomputer system 11 for each frame and this information is repeated on acontinuous basis depending on the refresh rate of the color camera 12.The way the color information is processed by the software program ofthe computer system 11 is explained in details below.

The color identifying method can identify six (three factorial) colors;red, green, blue, yellow, cyan, or magenta with the use ofthree-component color camera 12 as well as black and white for a totalof eight colors. With the advance of the four-component color cameras,24 (four factorial) colors or a total of 26 colors including black andwhite can be identified. The present method identifies the colors ofinterest on a target image accurately under varying light and imageconditions.

As a first step, the method receives the output information of thecamera expressed in (R, G, B) values of color components of each pixel.The largest color component is then identified and all threecolor-components (R, G, B) are divided by this value. It is important tonote that the largest color component may be different from pixel topixel and is not an overall or fixed maximum. In this way, the presentmethod creates a new color space called “Ratio Space”. The components ofthe ratio space (r, g, b) are such that the largest component is always1.0 and the other two components may be 0 or 1.0 or a value between 0and 1.0.

From this point on, the method processes the color information from eachpixel in ratio space values (r, g, b). Next, the ratio space values (r,g, b) are put to a “Threshold Test”. If the values pass the thresholdtest then the information is identified as a “rich” shade of the colorof interest. The present method departs from the prior art in that theprior art tries to identify every shade of a color on the target imageby matching that color to an elaborate library of reference color imagesor templates. The improved method effectively and accurately identify“rich” shades of a color of a target image from the “pale” shades of acolor under varying light and image conditions. Once the relevant pixelsare identified as “rich” shades, the adjacent pixels are clumpedtogether to form blobs and these blobs are then filtered by geometriccharacteristics such as shape, size, location, orientation, etc.

The method then keeps track of the information of a target image fromone frame to the next. Any changes in the target image from one frame tothe next or succession of frames can be used as an interaction betweenthe user and computer. This interaction can be in the form of performingcertain tasks or initiating applications or feedback, thus making thecamera a convenient interface for the user. Thus, the first step intracking is filtering out of the clutter of the target image all but aspecific rich color. Next, this simple image is filtered to find blobsof this color with specific shape and size. This step is repeated forother specific rich colors. And finally, a target or set of targets ofthat are geometrically related to each other can simply be identifiedand used to trigger a computer action.

The threshold test is carried out in a “Distance” equation definedbelow. The distance equation converts color information from each pixel,in ratio space values (r, g, b), to “achromatic” color information(black, gray, or white) between 0 and 255 or more preferably to “binary”information black or white (0 or 255). The method creates a “Filter” bycombining the threshold test into the distance equation and accomplishesto reduce the color information of a target image into a binary output,black or white. Black represents the color information that passed thethreshold test as a “rich” shade of a color of interest or “target” andwhite represents the color information that failed the threshold test asa “fade” shade of a color or “unidentified” color. Thus, with athree-component color camera, the method can separate a target imageinto 6 regions of distinct colors.

The distance equation employs a “Scale Parameter” (S). The scaleparameter is usually a very large number and set to a “negative” valuefor the primary component(s) of the color of interest so that itoperates in the opposite direction to the “Threshold Value” (T). Thedistance equation also employs a function called POS (q) and POS (q)=0if q≦0 else POS (q)=q. The distance equation is defined as follows inratio space color component values (r, g, b):Dist=POS(Sr*(r−Tr))+POS(Sg*(g−Tg))+POS(Sb*(b−Tb))

The preferred threshold values and scale parameters for 6 colors ofinterest are as follows:

RED: Tr=1.0, Tg=0.8, Tb=0.8 Sr=−1000, Sg=1000, Sb=1000

GREEN: Tr=0.8, Tg=1.0, Tb=0.8 Sr=1000, Sg=−1000, Sb=1000

BLUE: Tr=0.8, Tg=0.8, Tb=1.0 Sr=1000, Sg=1000, Sb=−1000

YELLOW: Tr=0.95, Tg=0.95 Tb=0.8 Sr=−1000, Sg=−1000, Sb=1000

MAGENTA: Tr=0.95, Tg=0.8, Tb=0.95 Sr=−1000, Sg=1000, Sb=−1000

CYAN: Tr=0.8, Tg=0.95, Tb=0.95 Sr=1000, Sg=−1000, Sb=−1000

The method can also determine the achromatic colors such as black andwhite when all three color components in ratio space (r, g, b) are 1.0or nearly 1.0, if so by looking at the original (R, G, B) values being(large) above a white threshold or (small) below a black threshold.

For a given pixel of color information, if the output of the distanceequation is 0 then that color passes the threshold test, if the outputof the distance equation is anything but 0 then that color fails thethreshold test.

The following example demonstrates how distance equation filters thecolor information from the camera output to binary color information:

EXAMPLE 1

Consider Two Pixels with the Following Components:

Pixel 1: (R, G, B)=210, 50, 40 and Pixel 2: (R, G, B)=210, 190, 80

In ratio space values: Pixel 1: (r, g, b)=1.0, 0.238, 0.190 and Pixel 2:(r, g, b)=1.0, 0.904, 0.381 then the distance equation for the Pixel1and Pixel2 become:Dist1=POS(−1000*(1.0−1.0))+POS(1000*(0.238−0.8))+POS(1000*(0.190−0.8))=0+0+0=0Dist2=POS(−1000*(1.0−1.0))+POS(1000*(0.904−0.8))+POS(1000*(0.381−0.8))=0+10.4+0=10.4

The result of distance equation is “0” i.e. the Pixel 1 passes thethreshold test and is identified as a rich shade of red and the outputof the filter is set to black. On the other hand, Pixel 2 does not passthe threshold test and is categorized as a fade or pale shade orunidentified color, therefore, the output of the filter is set to white(i.e. 255).

There are several ways for defining a filter and setting thresholdvalues. For example, a pixel representing a green color might registerthe following values in the ratio space: (r, g, b)=0.45, 1.0, 0.55. Afilter can be constructed such that anything with Tr≧(1.45/2) or Tg≦1.0or Tb≧(1.55/2) is rejected by the filter. This threshold is called the“half-distance-value” to the primary color component (1.0).

The method can be enhanced to handle cameras that are not calibratedcorrectly for the ambient lighting. This requires a preprocessing phasethat consists of the following steps: First, identifying the componentbias of each color component (R, G, B). This can be done by red, green,blue targets or a set of known black blobs and identify the lowestcomponent values of each of these colors. Subtract each of these threevalues from their corresponding component in each pixel of the entireimage. Second, multiply each R, G, B value of every pixel in the imageby a single scale factor so that the entire image brightness is enhancedto compensate for the brightness that was subtracted. For the ratiosignature space this step is unnecessary since the ratio cancels out anyfactor that is common in both the numerator and the denominator.

To provide successful commercial applications in color identification,the method should be very robust in every lighting condition. A field ofview might be under direct sunlight or in a shadowy room or underincandescent lights during evening, etc. The strength of the method inidentifying color particularly in challenging lighting environmentscomes from the “Ratio Space”. The ratio space has an impact on findingtargets and colored objects in a typical environment for commercial andconsumer applications. The following example illustrates this point:

EXAMPLE 2

The camera output might register (R, G, B)=0.6, 0.8, 92.8 and (r, g,b)=0.006, 0.008, 1.0 for a blue spot over a sunny part of the field ofview or (R, G, B)=3.2, 14.3, 63.5 and (r, g, b)=0.05, 0.225, 1.0 over ashadowy region of the field of view. The camera output for a red spotmight register (R, G, B)=99.6, 0.4, 0.4 and (r, g, b)=1.0, 0.004, 0.004over a sunny part of the field of view or (R, G, B)=64.7, 17.8, 4.6 and(r, g, b)=1.0, 0.275, 0.07 over a shadowy region of the field of view.While the original (R, G, B) values might fluctuate significantly fromsunny regions to shadowy spots of the field of view, the ratio spacevalues make it easy to identify the color of interest.

Another advantage of the present method in identifying color is theability to optimize the “camera parameters” for varying lightingconditions. Camera parameters such as: gain, brightness, contrast,saturation, sharpness, white balance, backlight compensation, etc. canbe optimized for a given field of view and the accompanying lightningconditions. The method accomplishes this optimization by going through acalibration process for a known field of view as a preprocessing step.Once the camera parameters are optimized for a given field of view, themethod is ready to launch.

The field of view 13 for the present method can be anything that thecamera 12 is pointing at. The camera 12 can be pointing at a desktopsuch as in FIG. 1, and in this case, the field of view 13 can be a plainsheet of paper, a book, an object, etc. The camera 12 can be pointing ata person or people in front of the computer, or a scene with items orobjects in it. The field of view 13 can be a screen or whiteboard thatthe camera 12 is pointing at. Further, the target image that isprocessed by this method can be the entire field of view or part of thefield of view such as an “area of interest”. For example, not every itemor object in the field of view might be changing from one frame to thenext. In this case, the target image might focus on the section of thefield of view that might be an area of interest.

It should be by now obvious to one skilled in the art that the presentmethod can be used in a variety of consumer and commercial applications.One aspect of creating consumer friendly applications using the methodis the ability to identify color effectively under varying lightingconditions in the field of view of a camera. The monitoring and trackingchanges in the field of view of a camera lead to potential uses not onlyin traditional machine vision applications but also open up consumerapplications with the use of inexpensive webcams.

FIG. 2 illustrates a computer vision system that performs color trackingaccording to one aspect of the present method. An application of thepresent method is given in FIG. 2 as an example for tracking an object20, such as a pen, in the field of view by identifying relativelocations of colored sections of this object. Tracking of simple objectssuch as a pen or finger in a field of view can be used as an alternateinput device for computer aided design and drawing CAD) applications.

1. A method of identifying color comprising the steps of: using at leastone processor to perform the steps of: defining a ratio color spaceincluding determining the largest color component value for each pixelin an image; and dividing all of the color component values of eachpixel by the largest color component value of each pixel.
 2. The methodof claim 1 further comprising the step of developing a filter for usewith a color space including the step of: creating a threshold black andwhite image for one color of interest in each pixel of an image; settingthe threshold so that pale shades of the color of interest are separatedfrom rich shades of the color of interest; and repeating the steps foreach color component in each pixel.
 3. The method of claim 2 furthercomprising the steps of: if any color component for each pixel of theimages is above or below the threshold, setting all three colorcomponents for each pixel to black; and if all color components of eachpixel of the image matches the threshold, setting all three colorcomponents for each pixel to white.
 4. The method of claim 1 furthercomprising the step of: performing a threshold test on the ratio ofspace values of each pixel.
 5. The method of claim 4 further comprisingthe steps of: if any color component for each pixel of the images isabove or below the threshold, setting all three color components foreach pixel to black; and if all color components of each pixel of theimage matches the threshold, setting all three color components towhite.
 6. The method of claim 4 further comprising the step of:converting the color information for each pixel, in ratio space values,to achromatic color information.
 7. The method of claim 6 furthercomprising the steps of: reducing the color information of a targetimage into black and white, with black representing color informationthat passed the threshold test as a rich shade of a color of interestand white representing the color information that failed the thresholdtest as a fade shade of a color of interest.
 8. The method of claim 6wherein the converting step further comprises: employing a scaledparameter for each primary component of a color of interest.
 9. Themethod of claim 8 wherein the step of employing a scaled parameterfurther comprises the step of: setting the scale parameter to a negativelarge value for primary components for the color of interest.
 10. Themethod of claim 9 wherein the converting step further comprises the stepof: solving a distance equation in terms of ratio color components foreach pixel of the target according to:Dist=POS(Sr*(r−Tr))+POS(Sg*(g−Tg))+POS(Sb*(b−Tb)) where Sr, Sg, and Sbare scale parameters for the primary color component values r,g and b,Tr,Tg and Tb are threshold values for each of the color components r,gand b; and POS (q)=0 if q is less than or equal to 0, POS (q)=q.
 11. Themethod of claim 1 wherein color component values are included in RGBcolor space.
 12. A robust method for rapidly finding the location of acolor target within an image, the method comprising one or more of thesteps of: using at least one processor to perform the steps of: as a onetime step prior to processing a set of images, defining a color ratiospace and creating a corresponding look-up-table for each primary colorand secondary color used in a target; capturing an image and subtractingfrom each pixel in the image the bias of each camera color component;applying the look-up-table to each pixel in the image for each primaryand each secondary color used in the target; clumping together theadjacent color information of adjacent pixels to form blobs of the samecolor; filtering the blobs of the same color by at least one of shape,size, location and orientation; sorting all of the filtered blobs tofind targets that have of a predefined pattern formed of a set ofdifferent colored blobs with at least one of a specific size and aspecific shape at relative locations and orientations to each other;tracking a plurality of targets to determine one of the absolute and therelative location and to determine if any changes occurred in one of asequence of images and compared to an ideal image; and using the changesof interest introduced to the field of view as a triggering mechanism toperform one of running macros and applications and initiatinginteraction between the user and the processor.
 13. A method foridentifying color information of a target image in a field of view of acamera on a pixel by pixel basis, the method comprising the steps of:using at least one processor to perform the steps of: receiving thecolor information of camera output in three color component values foreach pixel of the target image; defining a ratio color space bydetermining the largest value of the three color component values anddividing the three components value by the largest value for each pixel;defining the components of the ratio space such that the largestcomponent value is always 1.0 and the other two components are any of 0,1.0, and a value between 0 and 1.0; putting the ratio space values ofeach pixel to a threshold test; identifying the color information ofeach pixel as one of rich shade of the color of interest if it passesthe threshold test and as a fade shade of the color of interest if itfails the threshold test; filtering the color information of each pixelinto a binary black and white outputs, with black representing colorinformation that passed the threshold test as a rich shade of a color ofinterest and white representing color information that failed thethreshold test as a fade shade of a color; clumping together the blackoutputs into black blobs for each color of interest in a white matrix ofthe target image; sorting the black blobs of the same color by at leastone of shape, size, location and orientation as targets and generating aplurality of distinct black and white outputs of the target image foreach colored of red, green, blue, yellow, cyan, and magenta; andidentifying the information targets may carry with respect to each otherat least one of color, shape, size, location and orientation.
 14. Amethod of identifying color in a color space comprising the steps of:using at least one processor to perform the steps of: determining thelargest color component for each of a plurality of pixels of an image;subtracting each color component value in each pixel from the largestcolor component value; and normalizing the resulting values.
 15. Themethod of claim 14 wherein the normalizing step further comprises thestep of: multiplying each color component value by a scale factor. 16.The method of claim 14 wherein each color component value is included inRGB color space.